effect_plots: Create effect plots

In villegar/MetaPipe: A High-Performance Computing Pipeline for QTL Mapping of Large Ionomic and Metabolomic Datasets

Create effect plots

Description

Create effect plots for significant QTLs found with qtl_perm_test.

Usage

effect_plots(x_data_sim, qtl_data, cpus = 1, plots_dir = tempdir())

Arguments

x_data_sim	Cross-data frame simulated with qtl::sim.geno.
qtl_data	Significant QTL data.
cpus	Number of CPUs to be used in the computation.
plots_dir	Output directory for plots.

See Also

qtl_perm_test

Examples

# Toy dataset
excluded_columns <- c(1, 2)
population <- 5
seed <- 1
example_data <- data.frame(ID = 1:population,
                           P1 = c("one", "two", "three", "four", "five"),
                           T1 = rnorm(population),
                           T2 = rnorm(population))

example_data_normalised <- 
  data.frame(index = rep(c(1, 2), each = 5),
             trait = rep(c("T1", "T2"), each = 5),
             values = c(example_data$T1, example_data$T2),
             flag = "Normal",
             transf = "",
             transf_val = NA,
             stringsAsFactors = FALSE)

out_prefix <- here::here("metapipe")
example_data_normalised_post <- 
  MetaPipe:::assess_normality_postprocessing(example_data, 
                                            excluded_columns, 
                                            example_data_normalised,
                                            out_prefix = out_prefix)

# Create and store random genetic map (for testing only)
genetic_map <- 
  MetaPipe:::random_map(population = population, seed = seed)
write.csv(genetic_map, 
          here::here("metapipe_genetic_map.csv"), 
          row.names = FALSE)
# Load cross file with genetic map and raw data for normal traits
x <- qtl::read.cross(format = "csvs", 
                     dir = here::here(),
                     genfile = "metapipe_genetic_map.csv",
                     phefile = "metapipe_raw_data_norm.csv")
set.seed(seed)
x <- qtl::jittermap(x)
x <- qtl::calc.genoprob(x, step = 1, error.prob = 0.001)
x_qtl_perm <- 
  MetaPipe::qtl_perm_test(x, n_perm = 5, model = "normal", method = "hk")
x_sim <- qtl::sim.geno(x)
MetaPipe::effect_plots(x_sim, x_qtl_perm)


# Clean up example outputs
MetaPipe:::tidy_up(c("EFF-", "LOD-", "metapipe"))

villegar/MetaPipe documentation built on Nov. 22, 2022, 10:44 p.m.